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 Welcome to this Week in Health, it influence where we discuss the influence of technology on health with the people who are making it happen. My name is Bill Russell. We're covering healthcare, c i o, and creator of this week in Health. It a set of podcasts and videos dedicated to developing the next generation of health IT leaders.

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And then one last thing before we get to our guests. We have two new services I wanna make you aware of. One is this week Health Insights. Uh, we made the, the commitment about 18 months ago to invest in the careers of people in health. It, and, uh, a a lot of health leaders have, have stepped up and been a part of that.

Now we've packaged that up, uh, packaged that wisdom up so you can receive it in your inbox every Tuesday and Thursday. I take a short snippet from these interviews, uh, and answer the question of, so what? Try to make it pragmatic. Something you can apply to your career today. You can visit this week, health.com/insights.

And then the last thing before I get to our phenomenal guest for today. This week Health staff meeting is a result of a conversation I had with a couple of, uh, CIOs and, uh, that launches in two weeks, and it's designed to expose your team to new thinking and get the conversation going. Essentially, what that's gonna be, again, is a short snippet from Industry Leader with two discussion questions.

We're gonna send out one of those per week and you can share it with your staff. Uh, have the conversation and get it, get your staff meeting kicked off on the right foot. You can pre-register for that at this week, health.com/. Staff meeting with no spaces. Okay, enough. Um, today I'm joined by two guests, one returning, and one new guest to introduce to the community.

Uh, Ann Weiler is the, uh, c e o of well Pepper, and a returning guest who brought along the c t o and co-founder of Well Pepper, Mike Van Snellen. Uh, good morning both of you. Welcome to the show. Ian, did I get your name right? Got my written name right, but there's no er on Ben Snellen. Oh gosh. Sorry. There are much worse ways to put my name.

So let's, at some point, at some point, I'm gonna have to get the names right before I have a show with you guys. Well, peppers. Well Peppers still the name of the correct. The, the company. Yes. I messed that up too. Uh, this what happens when I wear a tie, I get all flustered. Um, wow. I, well, and this is the first of the shows that I'm doing with two guests.

And, uh, as you guys know, this is, you know, sort of a balancing act, not only because Anne is actually standing on a server to do the show, so the height disparity isn't too bad, but it's a balancing act to, you know, to get both of your perspectives on a lot of different topics. So I'm looking forward to this.

Um, the topics we, we discussed ahead of time, we're gonna talk about machine learning, data protection, data bias, big tech privacy, a bunch of other stuff. And we're gonna do all that. Try to do all that in a half hour. Are you guys, you guys ready for this? We're ready. Yeah. And with the slant of patient generated data, 'cause that's what we're all about here, patient.

Uh, absolutely. That would be great. So, and, uh, let's start with a quick update on, well, pepper, uh, what, if anything, has happened or changed since the last time we spoke, which was at hims? Uh, well I got over the flute, you remember I had that. Hence flute. Um, but yeah, lots of stuff. Um, we did a, announced a partnership with Evid Dion to do inpatient systems, and that's as a result of customer demand.

What we're seeing from customers is wanting to have a few integrated systems that really cross the whole patient experience. And you're seeing that in health systems, hiring Chief Digital officers and really thinking about this end, end, uh, we love the folks at Eon. They've taken a very patient-centric approach, strong product architecture, and it was just a, a great fit to announce that partnership.

Uh, that's one of the things. Uh, we got the results. Uh, two of our studies have.

Study with Boston University, people with Parkinson's disease, um, and a study with Harvard, uh, for seniors who were at risk of adverse events. And so both of those have been published. Some of the great highlights I can share from those is, uh, the Harvard study, uh, people had improved mobility by working with a remote care plan and a, um, remote interaction and check-in.

So they improved mobility. But more importantly, they had reduced ED visits, so true cost savings that you can prove, um, from these interventions. And then our, our friends at BU we, we deployed and launched a new study with them a. I for a broader Parkinson's study that includes a behavioral health component, so not just the medical and physical clinical intervention, but the behavioral health side of it as well.

Um, so very excited about those studies. Very important to us to, to prove, you know, independently prove that what we're doing works in empowering patients to follow their care plans. And then for our customers, we've deployed new scenarios in orthopedics, um, shoulder and a c l surgery. In neurology, new headache care plans, and we just, um, deployed, uh, an Alzheimer's intervention.

So again, proving that, you know, patients can and will self-manage if you give them the right tools. But also our platform approach, which was basically that a patient engagement experience needs to support all patients, whatever age they are and all conditions. Wow. Uh, so a ton has happened. Uh, can you provide me those links?

I'll, I'll include 'em with the, uh, with the podcast and the video because that's, uh, that's really important research that's essentially saying, Hey, we have digital interventions that are producing results. That's essentially what those studies are gonna, are showing us. And, um, you know, we've taken the.

Theory that patients can and will self manage if you give 'em the right tools. The BU study patients who came into it with a lower patient activation score, so that's basically they feel less confident about their ability to make, follow their care plan. They saw the greatest gains. So again, you know, that goes back to the, sometimes when you talk to clinicians, they say, it doesn't matter what I tell the patients, they won't do it.

That's not true. It's about how you tell them, how you support them and how you basically, you know, provide them that link back to the care team if they need additional help. Fantastic. Well, Mike, we're gonna introduce you to the, uh, to the, uh, community here. Uh, you wrote a really cool article on, uh, self-driving healthcare and the, the concept was around machine learning and data and, and, and essentially how to, uh, help healthcare have its self-driving moment.

Uh, give us a little background on or premise of the article, uh, before we jump in. Thanks, bill. I'm happy to be here. Um, long time listener. First time caller, I guess. Um, so yeah, I mean, the premise of the article is like we're, we're super excited about the opportunity for machine learning and healthcare.

Uh, and one of the things that we discovered when we started looking at, you know, what the possibilities were is that a lot of the data that you have in healthcare is really still siloed in the. Um, and so you have a lot of billing data and diagnostic data, um, and that kind of data is good for some things for machine learning, but really doesn't enable healthcare to have, uh, really a robust mach, you know, self-driving moment in the same way that, um, machine learning is helping, uh, to propel the, the self-driving car, autonomous car vehicle industry.

Um, in self-driving cars, there really was no such thing as a concept of a self-driving car. And it was really, you know, these large, uh, government studies that were, you know, millions of dollars to trying to get a car to drive, uh, short distances. Uh, and really the, the presence of data was what catalyzed that movement in self-driving cars, um, where you could get a lot of data about the environment and about.

Um, and the, the problem we have in healthcare is we just don't have that kind data about patients when they're out on the in, happens inside the four of the hospital, but not a lot.

So you can think about like the difficulty of, let's say all you had was maintenance records of a car and measurements and tried to train aiv data, need a robust data. And so that's what we've been focused on. Uh, that data set of, you know, how do patients interact, uh, with the health system when they're, um, you know, between visits and using that data set in the future, be able to help augment and eventually maybe start automating care for patients.

So, so let's stay on cars for a second just to get the context right. So, machine learning is a subset of ai, but machine learning is the act of training the machine by example. So it. Uh, it, it learns by example. So I guess from your article about 15 years ago, they had, you know, government maybe sponsored studies that, you know, you had, uh, universities out there with teams and they were trying to get their cars to drive, you know, 10 miles, 50 miles and those kind of things.

Um, and then the, the big breakthrough was Stanford. And, and they essentially taught their car to learn by example. Can you give us a little idea how that works? And then we'll, we're gonna come back to, to healthcare. 'cause that really brings up some of the biggest challenges of training machines with healthcare data.

Yeah. Um, yeah. So the, the way that, uh, self-driving cars evolved really was, as you say, the, the big breakthrough was using, uh, example driven data. Uh,

Sensor data and visual data captured from vehicles, uh, and not training by algorithm. Uh, there were many companies that tried, or many I guess, participants in the research that, that tried that approach. Um, where you try to have a smart person sit in the room and say, well, if you see these conditions, then do this.

Um, and instead of doing that, they said, well, let's just collect a whole bunch of data. We'll label which things are the good things, and the machine learn about how, how. Uh, and so it's a great technique when you have kind of very large input spaces of variables and, um, a, a set of things that you wanna to drive towards from, from that, which is actually a lot like healthcare.

You have a whole bunch of data about people, whether it's their genomic information, their diagnostic information, their diet and exercise activities of daily living. All of that data, if you were able to quantify it, uh, is a fairly large data set. And so it'd be really hard for a person to decide, well, you know, here, here's, I'm gonna weight this factor so much and weight this factor so much.

Um, but machines are really good at that. If you said, you know, here's a million people and we can observe from all of this data that we collect about these people, you know,

We can start to learn from that data and build predictive models, uh, for, for people's health. But is it quantity of data or is it quality of data? I know the answer should be both, but which is more important? Both. I mean, certainly, uh, machine learning works best with large quantities of data. It's, uh, notoriously data kind.

Been tweaked towards billing purposes. So for example, we see a, a predominance of, uh, procedure codes that skew towards higher billing rates. Um, even though that may not be a fair representation of the observed diagnostics and the observed procedures that are actually needed for patients. So there are some kind of data cleanliness.

Uh, that you need to overcome with when working with some of the, the data sets that we have in healthcare today. So if you look at, at what we're doing, we've got a contained experience. So we've got patients who are, we know what disease or condition they have based on the care plan that they've been assigned, uh, and we've got.

The things that they've been assigned to do. So we know what they're supposed to be doing and we know how they're doing against that care plan. So with a smaller data set, we can start to get insights from this patient generated data, which is very different than saying, you know, there's one, one approach that says, get as much data as you can and try and find some sort of insight in it, which is great if you're, you know, a large academic researcher.

Um, and you can keep doing regressions on that to start to find trends. We can start from a hypothesis. So for example, we've got somebody on a, a total joint replacement care plan after surgery, we can say, all right, we're gonna look for factors that might predict readmissions. Well, we've, you know, we've already got a smaller surface area.

We don't need quite as much data to be able to find those things that might indicate an adverse event. And so we've actually trained a machine learn message classifier on. Patient generated messages for patients who are part of that sort of post ambulatory. Surgery care plan and been able to say, Hey, this, this type of message seems, seems like it might be a problem.

And so you can do things if you know that, you know, if the good data in, you know, if the data's good in and you know what the data is for, you can start to find these insights a lot faster. And I think that's, you know, some of the, the challenges today of machine learning are people are just trying to take these massive data sets.

The m r that weren't necessarily developed for those insights and trying to find those insights. Now you know that's doing that Microsoft.

And enough money , quite honestly, to do that. I think where, where we're looking at it and where health systems should be looking at it is a little more contained. And I, I think you see that with health systems. They, they're looking at things like, how do we fix sepsis? So, you know, what's this, you know, rather than what's all the data, what's the insight?

Going back to the way we're looking at it, which is on this care plan, what are, you know, what's gonna indicate someone's about to not be adherent? What's gonna indicate that they might have an ED visit? All of those things. We, so here's the distinction. I I, I want you guys to sort of draw, like, we've been doing this for years, but we do it.

People, people sit down, look at the data and say, oh look, this correlates with this, this correlates with this, and those kind of things. How does a machine sort of step in the middle there and how do you focus it in on just a, a few variables to say, you know, so that it's actually picking those things out.

Alerting you not the well pepper team sitting in a room going, Hey, I'm seeing this trend in this. Well, it does start with the well pepper team sitting in a room, right? Like someone, a human needs to label the data to begin with, which is kind of the, the secret of machine learning and ai. And you know, when everyone was very upset about the fact that there were people listening to utterances on the home devices.

People pre, previously are reading, you know, your, what you've typed in to actually apply Ries, for example. So it.

I'll turn over to Mike. Yeah, I mean the, yeah, the data, data labeling can be a fairly intensive process, and it's hard in healthcare too because the, like the industry approach to data labeling in a lot of cases, uh, is not very well, it's not very privacy savvy. Today we've seen some press coverage of, uh, some of the issues with both Amazon and.

Group call group sourcing of people to go and label data. Um, but then it becomes input to, uh, a machine learning training algorithm. Um, in healthcare, we have, be careful about that because, you know, for example, the message classifier that we train, we looked at the data and said, well, there's a possibility that there's.

There's a, like a sensitivity there with healthcare data, but how you perform data labeling. Um, but then after that it's kind of the magic of machine learning. And there have been articles about, you know, what, why is machine learning so unreasonably effective at doing what it does in image recognition, in diagnostics, in machine, you know, self-driving cars, uh, is, don't understand why's.

One of the trade off is we don't always know why it's visibility train machine.

How build models that are not just accurate, but also predictable. Like we can explain why they predicted what they predicted, which is also, you know, challenging in healthcare because, you know, you get, there's a lot of inconsistency today and you know, first of all, people don't want something prescribed to their patient unless they understand why it's been prescribed and you know, sometimes we'll see.

Three surgeons and you get three different opinions. I mean, there's a whole idea of getting a second opinion. So having that kind of closed box, we don't know where this came from, is challenging, which is why, again, you know, it's gotta be this kind of baby steps. The the steps have to be, all right, let's see what things we can identify, and if we can identify 'em, then let's use that to scale the clinicians, right?

So present to the clinicians, Hey, we've identified this patient might be a greater risk of readmission. And then maybe suggest to them some actions that they can take or maybe just say, we've identified this, and it just is enough of a flag for some, a person to reach out and see what's going on. Um, so, you know, it absolutely cannot be without people in healthcare.

Yeah, absolutely. The, the conversations we've had around AI previously, just the, the physicians who we've had talking about it have said, you know, I, I just think about it as, um, you know, just another opinion. So it sort of comes through and says, Hey, look at this. And I say, all right, well that's the computer's opinion.

Based on this data. Here's my opinion and I'm gonna get another consult just to, to weigh those things. Uh, you know, the other thing that was fascinating to me was, Um, we, we did one of those projects, uh, you know, our team all had, you know, their own personal time to do projects and, and two of the players went out and just got, you know, TensorFlow that they just brought all this stuff in and then they fed the, the images through and they, the results of the images through.

And they were starting to predict, I mean, these are just two people sort of hacking in the corner of an office who have access to, to obviously healthcare data. But they were going through it and it. They were approaching, you know, 80, 85, 88, 90% accuracy on image reads after like a month of work. Yeah.

Which, I mean, sort of said, it sort of talks to your, you know, it is effective. We're, we're not, we're not entirely sure what's going on behind there, but it, it's pretty, it's pretty interesting that it's that effective. But let's talk about bias for a second. So, How does bias present itself? I assume it presents itself through the data.

And then what, what are we doing about bias? How do we, how do we prevent bias? Uh, , I mean, Mike, Mike pointed this out like the, the E M R data is bias because it's bias towards billing for the most part. And, you know, unfortunately, a lot of the data is, is not clean. You know, there's. You've seen the, the burdens of documentation stuff gets copied and pasted stuff is like, you know, we're gonna, we know that this, you know, this billing code applies and the patient is, sort of, has this, but like the number of times, I'm sure you've had this as a patient where you know, someone is reading something back to you and, and saying, is this correct?

And you're like, No, you know, the one that always gets me is when they read the medication list. I'm like, are you still taking amoxicillin? Well, of course not. You know, , that's a seven day course. So, you know, the data is missing a lot of information. Um, it's missing what happened really with the patient.

It's missing what's happening with the patient in their daily life, which is those activities of daily living are the things that really affect your health. Um, it may also be missing. Opinions of the full care team. So you know who, who has actually done the documentation in the m r and you know, I was thinking back, you know, we don't know how it does it, but it does it think about like when you, you see doctors, you know, especially new residents are told if you see, if, you know, if a nurse thinks something is wrong, believe that nurse.

So, you know, is that. You know, that hunch that a nurse had, that a patient is about to code or something like that. Is that in there too? So are what is the, when we're looking at, you know, we'll, we'll look at patient, when we're looking at patient data, is it the full spectrum of what's happen happening with that patient?

And then also, you know, are we exacerbating human bias? You know, I. And one of the, you know, one starting this, this company was a result of an experience that my mom had. Um, she actually faced a lot of bias before she got diagnosed, and there was bias, I suspect about her age. And possibly, you know, you, I just finished reading a book that I, I blogged about, about data bias.

That there's a lot of data missing on women, um, because studies were not done on, you know, Some famous studies where there were problems, the thalidomide example, but then because of that there's a whole lot of data missing. And so, you know, a heart attack presents differently in a woman than a man. But we don't actually have the data to show, you know, like as a woman, I'm reading this thinking, how will I know if I'm having a heart attack?

And it seems like we won't . So I think that the thing we've gotta recognize before we go too far, Is this, do we have all the data and how do we get more data? And that's what, you know, we're quite passionate about. The fact that the patient generated data, the experiences of, you know, why didn't you take your medication?

Was it a side effect? Was it, you know, you couldn't afford it? All of those, those additional things around it, you know? 'cause you could come up and say, well, our data shows that patients on this condition just don't take their medication. Well, that's only part of the information. Why didn't. Is it something to do with the side effect?

Is it something to do with the, the condition that they have? So I. I am worried that we got a little too far ahead of ourselves. Like we need to stop and say, okay, if I'm feeding this data into the machine, what bias might that data have just to begin with? Because it's missing something. And think about all the questions that you can ask and how can you get that additional data.

We think that you can get a lot of data from patients. Um, I suspect, you know, that's 'cause we're, that's where we're focused. We do get feedback from clinicians as well. What action did take when a patient presented a certain alert? Um, but really thinking about like all of those things that are happening and how do you get everybody, I wanna say everyone's viewpoint, but you know, there's a lot of stuff going on.

So a, a picture of someone's health is greater than their e H R data. We know that, uh, whole person profile is necessary. Um, you, you're, you have a bias towards collecting the data directly from the patient. Go figure. That sort of makes sense. Um, but where, where are we going? Are, are there other sources of data that we can sort of tap into?

Um, I mean, and, and even, are there some limitations in the, in the patient generated data as well? I guess those are two questions. Yeah. Limitations. And then are there, are there, uh, industry initiatives that are starting to aggregate, um, information outside the E H R itself that are valuable that we can tap into?

Yeah, I mean there's definitely limitations in the data that you collect from patients and, uh, I mean some of those are kind of obvious things like, well, will the patient, you know, actually respond to all these surveys and questionnaires that we're percentage.

There are, um, you know, there's some percentage of, of the data you get from patients that you know may not be totally clean. Uh, the, the data that we have says that patients, when you ask patients kind of continuously and ongoing, uh, about, you know, to report their condition and things that are happening, uh, they're more likely to be truthful if they're reporting it in the.

Fill out a retrospective form of, you know, what symptoms did I have? They tend to misremember things in that scenario. So we believe in kind of trying to capture things as real time as possible helps keep that data as clean as possible. Um, and like there are a ton of, uh, data, new data sources that I think are, are starting to feed into healthcare.

There's a.

Uh, weight data or blood glucose, or there's aton of information that we can capture about, about patients. I don't think we know yet what that data means, and that's kind the classic problem today when you have, you know, pur reports of patients that bring in their Fitbit printout to their doctor and say, well, what does this mean?

And doctors, I have no idea.

Data, merging it with all the other things we know about the patient to draw meaningful clinical insights. Um, and then the other big piece is, uh, on the genomic side, you know, there's a lot of companies like the, uh, project baseline initiative from VE that are starting to study that. Like, let's look at a whole bunch of people.

Let's take their genetic information as well. All of you know,

A super meaningful and useful data set that'll take decades to collect. Um, but you know, the, the way we practice medicine in 20, 30 years will be different than the way we do today. Because of that, I think we're also seeing the, the government making data sets available as well to compare to. Um, I think definitely I don't see the two things coming together.

Again, this is like, I think we're in the stage where there's lot of data and people aren't taking this data and. It's like, you know, there's one group who's looking at claims data, there's another group who's looking at patient generated data, and it, it is hard to bring those things together and frequently, like, you know, when we're working with a health system, we're always looking at, Hey, how do we prove for you the outcomes that we're getting?

But also, you know, the outcomes can things like cost. Frequently, you know, the, the surgeons that we're working with to improve the outcomes are not the ones who have the cost data. I think that's the opportunity is trying to figure out how do we get, how do we match these data sets? How do we get them together?

And then, you know, there's also like a little, I don't wanna say chicken and egg thing, but you, you know, you saw with Propeller Health, they were able to predict air quality based on inhaler usage. Shouldn't we be flipping that around and think, okay, they, you know, the air quality's going to be bad or is bad, like, let's.

You know, that that was actually something that, um, a physician, um, in Kaiser said, said to me, um, because they do take a population health management, he said, this was in Seattle where we had last summer or some pretty, uh, terrible air quality from forest fires. And he said, how can I need to go tell all, excuse me, all my patients with asthma, what they should be doing?

And he had no way of finding that. So he. Environmental data, you know, every day we saw the air quality rating. And then he had no way of actually matching that with his patient data to say, all of you people, here are the things I want you to proactively do because this is gonna affect your health. So I think we're at the point of knowing that there are, are these large data sets, and knowing that there are, there's valuable data in individual data sets, putting them together that's, you know, that's gonna be the.

The thing that really breaks through on, on health. Wow. So still the best way for us to communicate bad air quality is through a sign on the road, a radio broadcast in the morning and TV news. Is that what I hear you saying? Well, I was always asking my phone. Uh, that was actually, that was my, you know, every day I wake up and be like, what's the air quality?

Siri did a really good job of that and would pull up like, tell me the number and then pull up a whole chart of what it was. But yeah, I mean that, but then you want, I don't know if I want Siri, but somebody then to say if I'm at risk, I mean, we were all at risk because it was so bad, but like, you know, Hey, here's what you should do proactively use your inhaler.

Stay inside. You know, people were, the news was saying, This is, this is how bad it is. And you'd see people jogging and they didn't know, you know, and that goes back and the Kaiser doctor was like, I wanna go tell people they. So let's, let's talk. Uh, I, I, I used the term Silicon Valley here and I probably shouldn't, since you guys are in Seattle and some of the players are in Seattle, so let's just call it, you know, the tech industry, their use of data.

So last week, Microsoft, Amazon, I b m, Oracle, I don't remember who else. Uh, they all sign their pledge to share healthcare data and I talked about it on Tuesdays Show. What does, what does this really mean? I mean, what do you, what do you think it means to the industry? Um, yeah, I mean, I think there's, uh, an increasing movement towards, uh, openness in, in patient data.

Certainly you've seen from some of the, the regulations coming outta C M Ss. Uh, towards giving patients access to their own data, I think is a, a huge step in the right direction. Uh, for too long we've had patient data that's locked inside of healthcare systems. Um, in terms of big tech using that data, I think, um, there's a a lot of good that's can come out of that.

Um, I think there's a, uh, a need for kind of the right privacy controls. Uh,

Or from those big tech players that health data is different animal than a lot of the other data that they deal with. Um, and it's, it's really easy to inadvertently leak. P h i even data sets that you believe may be de-identified, like there's certainly cases, uh, of, of people easily re-identifying data.

So, Um, I think with the right focus on privacy, I think there's, uh, it's a promising, uh, trend. There's a lot more good than bad that can come of it. Um, but I think there needs to be, you know, careful, uh, careful rules put in place by those health system or help big tech companies to kind of self-police, I guess.

All right, so let me, let me drive this conversation. So if you get, um, a certain part of the industry, those tech or those executives in the room, they'll say, look, Um, I'm all for giving the patients their data, but you know, they're gonna be, they're gonna be used in abuse. People are going to get that data, they're going to give it away.

They're, you know, we need to find ways to protect that data. And essentially what they're saying is they can't share the data with me 'cause they think that I, I'm not sophisticated enough to know how to protect my own data. I mean, what mechanisms are we going to put in? And you have it on your phone and Mike, you have it on your phone and your parents have it on their phones and, and, and now all of a sudden they have this great data set that you guys know how to use, and people who understand big data and machine learning and AI can start to create some really interesting things around.

But what they're afraid of is, Well, pepper and the good actors, they're worried about the nefarious actors who are already calling them every five minutes to try to get their social security number now getting access to their entire medical record. Yeah. It's an easy problem to solve, and it's not related to healthcare, like the number of people who have their email address with their date of birth in it.

You know, like the number of people who have their date of birth published on Facebook. Like there's some, it does seem like perhaps data privacy needs to be taught somewhere. You know, I, I feel like, you know, I'm, I'm gonna say particularly paranoid, but um, I remember. Um, I guess back in the nineties being concerned about who had my data.

And then Mike and I, um, worked at a company that was acquired by Microsoft and I was like, okay, well I can trust Microsoft now because I have to, because not only, you know, do they have my date of birth, they don't have my social security number, and they have my retirement plan. So, and my

everything about, but before who? Receive my information. I, I think that, you know, on the one hand it's my right to, I, I wanna disclose anything to you about my health. That's my right to do that. Um, you know, I wanna put my x I did put my x-rays on the internet when I broke my finger because I was, you know, I was like to blog about healthcare experiences.

Um, But at the same time, I think most people are not understanding about privacy in general. But on the other hand, you know, I'm talking myself in and out of things, but you know, on the other hand, no one's who's protecting our data, we look at some of the most recent breaches, um, things like Equifax, I mean that their whole job is protecting people's data.

So, you know, is it about educating people or is it about changing the systems? I don't know. At the end of the day, they're like, who? You have to think about whose data is it? If I'm a patient, is it my data or is it somebody else's? Um, and certainly when we in, in our contract, our standard language is that the patient data belongs to the patient.

We have license to use it for your care, but it's your data. And if you take the approach of the, the patient's data belongs to the patient. Policy decisions on kind of how and when you release in, in smart ways. But I think there's a fundamental kind of ownership question there. So it's that, that, that's interesting though, because, 'cause Mike, here's the, here's the counterargument.

'cause I get, I get too many of these discussions. Not that I agree with the counter argument, but it is a counter argument, which is if I'm sitting here taking notes about, uh, you and Anne and how well you're doing on the podcast, just because I'm writing about you, is that your data. Not necessarily, but what we have is patient generated data.

It's me saying, I took my medication, I'm feeling a little dizzy. I did this, you know what patient generated data, I'm, I'm choosing to tell you, this is not your interpretation. No, I, I agree with that. A thousand percent. Yeah. The question Yeah. When you're, when it becomes your interpretation of it as a doctor.

Yeah. That, that's another, but, but if the doctor is doing something to you, That seems to be your data. Someone has done something to you like, I don't know. That's a tricky one. And you look at like G D P R, the right to be forgotten. Oh yeah. That go with medical records if I wanna be forgotten. But then at the same time, medical records actually need to be kept so that we, you know, know what happens.

So I, it feels like we're as same with machine learning, like in the data privacy, it's almost like sometimes the. Regulations and the technology are and on totally different timelines. Yeah, I, yeah, and I've, I've said this on a couple of, uh, the news podcasts I do on Tuesday that Yeah, I'd like to press a button and have the, you know, essentially you download my health data to me, and then I would like for it to go out of your E H R and my case on that is,

I, this is gonna get me in trouble, but I do enough work within healthcare that I don't trust healthcare to protect my data. Um, there's just too many things changing. There's too many mergers and acquisitions, and every time you do a merger and acquisition, you connect to another health system that has a security profile and, and you just increase your attack vectors.

'cause the attack vectors primarily are the people you employ. And so when your health system goes from 20,000 employees to 40, you now have 40,000 attack vectors. To get into that health system. I, it, it's, I just don't think anyone's gonna say, oh gosh, I gotta target Bill Russell and get his health data.

That's not gonna be worth it to him. They want to get 20,000 records. They don't wanna get one. Yep. Yeah. So you made the case, you own it and you give people rights to it. So what are you gonna do with it? So we, so Sema Verma is successful and secretaries are successful. And, and, and actually even Joe Biden, Joe Biden actually was making this case around cancer research.

Yes. And saying, get that data out there. Okay. So the bipartisan, they're successful and say, all right, here's all the data. What, what are we gonna do with it? What are some of the best ideas or best thinking around this is what we can start doing for patients. If we can start aggregating all this data. I, I think it, oh, aggregating.

Sorry. I thought you were thinking about it. No, no. If, if, if I gave it all to you, if I had it and gave it to you, what are you gonna do for me? What am I gonna do for you? Well, your company . I think that's the big question, right? I think that's, uh, you know, if you take the approach of, uh, big data sets, uh, can lead to.

Know what all those areas are yet that someone's gonna discover, you know, links between, uh, health data and uh, zip code data and air quality and like, I think it is on researchers to be able to make good use of that data. And I don't know if we know all the places. We certainly see places where today, uh, machine learning is making early inroads on.

Um, but I wouldn't try to pre, I'm not enough where we'll,

So, lemme lemme rephrase the question 'cause this is, I, I really want to tap into your guys' expertise. You've started a company, it's successful, you're growing it, so we're just doing this little, you know, I, I know this, this little thing with studies from Boston University and Harvard and partnering with Mayo.

I, I get this little thing you guys are doing as a little side project, but with that being said, it has been successful so it. Um, what, what are, if, if you guys didn't have this going on and you were just getting into healthcare and all of a sudden, secretaries, Azar, they're all successful in all this data, what areas would you like to see a startup start to tackle or go after that you think, man, that could really have a significant impact In the community where I live, um, I, I would focus on seniors, um, because of demographics.

You know, we just don't have enough clinicians to care for everyone. So I would start to look at where can we really help people? What are the common things that people struggle with as they age, where maybe they don't need to be struggling if we knew certain things ahead of time. You know, everybody's concerned about Alzheimer's and dementia, but what can you do?

To help people be more independent because everybody wants to be. And then also, what can you do? Um, Their caregivers, whether that's, you know, their children or professional caregivers. And what I see right now in that space is a, you know, remote monitoring is a lot of like, the devices there. I don't know that the devices are , you know, that that's not enough.

There's, there's gotta be a lot more about what's going on and how do you help someone be proactive. Um, that, that's where I would focus. Yeah. Mike, Mike May focus differently. Uh, no, I mean, I think there's a lot of, uh, kind of clinical and diagnostic things that are super important and kind of very domain specific.

Um, I think one of the big challenges we have in healthcare is we just have an unsustainable, expensive model here in the United States, and so I think there's a lot around cost that we could do to understand, you know, what are the patterns of how patients.

Um, and I think that needs to shape policy about how we pay for things, uh, in, in healthcare and help kind of continue to move away from incident based payment and move towards kind of more value based payment. And having a complete understanding of like, what does value mean? If you understand, you know, how that patient progresses over a long course of time, not just with a few billing codes, but are they to be successful getting.

And I think as well, maybe if we can shorten the research, you know, like we've got the, the clinical trials and studies, which you have to do, and then we've got the insights from machine learning. How do we bring those two things together to shorten the cycle? Because right now it's someone publishes something.

It's 17 years before that gets into clinical practice. So like we know things today. That would improve everybody's life, but they're not necessarily in clinical practice. So how do you, how do you marry those two? Because the big data is, is also telling you something it would be, might be super interesting to, to match those analysis to studies that are, have already been done to further prove the study.

Fantastic. You guys, you guys are awesome. I love having you guys on the show and having you both on is, is, is, is That's why I, that's why I. I told him it would be fun. . Uh, and anything you wanna leave our listeners with before I, uh, close off here? Close out, uh, I don't underestimate patience.

That's, that's why you guys are together and that's why you're doing, doing the work that you're doing. Uh, how can they follow you guys? Just the well pepper.com at, at, well pepper on Twitter. Yep. It's probably the best place to get all our, our news. And then on our website we've got our blogs where we blog about all kinds of things.

Both, you know, things that we're doing, but general industry thoughts always related to the patient experience. Fantastic. Well, thanks. Please come back every Friday for more great interviews with influencers. And don't forget, every Tuesday we take a look at the news, which is impacting Health. It. This shows a production of this week in Health It for more great content.

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